1. Field of the Invention
The present application relates to multi-robotic systems and more specifically to a control architecture for a multi-robot agricultural field production system.
2. Description of the Related Art
The notion of improving productivity using autonomous agricultural vehicles has gained increased attention. A combination of labor shortages and the need to manage multiple machines by a single operator is driving the demand for the deployment of autonomous vehicles. While autonomous operation of these vehicles is beneficial to the producer, operating them in a farm environment is a challenging endeavor. Elimination of the driver from the control loop complicates contemporary automated agricultural vehicles adding to existing liability challenges. The agricultural environment in which an Agricultural Robot (ag-robot) operates is unknown, uncontrollable and unpredictable. Sensors on-board the ag-robot acts as a gateway to the external world and aid the robot in learning about the unknown environment.
The demand for application of computers and electronics to automate the agricultural field machinery has seen tremendous growth in the recent years. The primary objective of automating field machinery is to increase productivity. Although, electronics are extensively used in contemporary agriculture to assist machine operators, the continual increase in the size of farm equipment is creating problems with the metering and placement of crop production inputs and soil compaction. Automation is seen as a way to mitigate sources of errors and problems that are caused with the ever increasing size of human-controlled field machinery. Typically, use of multiple machines with dedicated human operators is common on most large scale farms. Hence, there is a one-to-one ratio of human operators to the number of machines. Use of agricultural robots and the capability of one human to manage and monitor multiple robots can reduce labor costs and increase the productivity of the farming operation significantly. Taking agricultural robotics a step further, deployment of a completely autonomous multi robot system (MRS) has the potential to be efficient, profitable and scale neutral. In the future, farm operators will monitor field operations remotely and respond to machine errors/failures as required.
Previous efforts by researchers have focused on implementation of MRSs for agricultural production. Algorithms for operating a leader-follower MRS were developed by Noguchi et al. (2004). In this system the lead machine is controlled manually and algorithms enable the autonomous follower machine to either follow or go to a particular location commanded by the lead machine. Vougioukas (2009) proposed a method for coordinating teams of robots where one lead machine specifies the motion characteristics of one or more machines (followers). Simulation experiments verified that the method can be used for coordinated motion of hierarchies of leader-follower robots. Researchers at Carnegie Mellon University and John Deere Company are working on a project to enable a single remote user to supervise a fleet of semi-autonomous tractors mowing and spraying in an orchard (Moorehead et al., 2009). In a similar effort, three autonomous peat harvesting machines performed 100 field test missions during tests conducted with end users (Johnson et al., 2009). The efforts to implement MRS by these researchers are a testimony to the fact that deployment of agricultural robotics in real-world applications is feasible and that production agriculture is evolving into a high-tech work environment.
The design of control architecture for a MRS becomes increasingly challenging with the number of robots performing a given task. The MRS should adapt to the changing environment and, changes in the configuration and capabilities of other robots to accomplish the overall production goal. A mechanism for dynamic coordination of multiple robots was developed using hybrid systems framework by Chaimowicz et al. (2004). The mechanism allowed the robots to dynamically change their roles and adapt to the changing environment to finish the task successfully. A framework and architecture for multi-robot coordination was developed by Fierro et al. (2002) for applications ranging from scouting and reconnaissance, to search and rescue. The software framework provided a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. In a similar effort, finite state automata theory was used for modeling multi-robot tasks for collective robotics by Kube et al. (1997).
In addition to task modeling and dynamic role assignment, coordination and synchronization of robot actions involve exchanging information between the team members to finish the cooperative tasks. Thus, inter-robot communication becomes crucial for the success of a MRS. Identifying the specific advantages of deploying inter-robot communication is critical as the cost increases with the complexity of communication among the robots. Three types of inter-robot communication were explored by Balch et al. (1994). They found that inter-robot communication can significantly improve performance in some cases, but for others, inter-agent communication is unnecessary. In cases where communication helps, the lowest level of communication is almost as effective as the more complex type. Rude et al. (1997) developed a wireless inter robot communication network called IRoN. The two important concepts of the network were implicit and explicit communication. Modest cooperation between robots was realized using implicit communication while a dynamic cooperation was achieved by using explicit communication.
To date, most of the research work done on multi-agent robot systems has been conducted in areas other than agriculture. Research work done on the architectural specifications of a MRS specifically deployed for agricultural production is nonexistent. Thus, there is a need to explore research and understand control methodologies so that multiple robots can be deployed simultaneously. Furthermore, a rapidly evolving contemporary agriculture industry may be poised to adopt MRS for increasing production efficiency.
Most of the initial work done on control architectures for mobile robots was carried out in aerospace and artificial intelligence research laboratories to accomplish military missions and space exploration. Several researchers identified the utility of control architectures that offered fault tolerance and robustness to the operation of these robots. A robust layered control system with task achieving behaviors was proposed by Brooks (1986) where, a behavior was defined as a stimulus and response pair. Brooks reasoned that the conventional sense-plan-act paradigm used in some of the first autonomous robots was detrimental to the construction of the real working robots. The author proposed a robust layered control system with task achieving behaviors in which each layer was an augmented finite state machine (AFSM) that works concurrently and asynchronously. Arkin (1989 and 1998) extensively utilized schema theory and developed distributive processing architectures for autonomous Behavior Based (BB) robots. Schemas were biologically inspired concepts that acted in parallel as individual distributed agents. Reactive and deliberative behaviors are two types of behaviors dealt with in BB robotics. Reactive behaviors are instantaneous responses of the robot to external stimuli where the robot's reaction is triggered by the changes in environment (e.g., reacting to obstacles). Goal-oriented/deliberative behavior is a pre-planned execution of control steps aimed at achieving a given task (e.g., path planning to reach a target). An Autonomous Robot Architecture (AuRA) for reactive control was thus developed by Arkin (1998) that consisted of five basic subsystems; perception, cartographic, planning, motor and homeostatic control. Numerous robot experiments and simulations demonstrated the flexibility and adaptability of this architecture for navigation. AuRA is a generic architecture that can be applied for navigation in buildings, in outdoor campus settings, in aerospace or undersea applications and in manufacturing environments. Yavuz and Bradshaw (2002) did an extensive literature review of the available robot architectures and proposed a new conceptual approach that included both reactive and deliberative behaviors. In addition to reactive, deliberative, distributed, and centralized control approaches, fuzzy logic and modular hierarchical structure principles were utilized. The architectures discussed above were mostly utilized for mobile robotic applications for non-agricultural settings.
For agricultural robotic applications, a specification of behavioral requirements for an autonomous tractor was provided by Blackmore et al. (2001). The authors discussed the importance of a control system that behaves sensibly in a semi-natural environment, and identified graceful degradation as a key element of a robust autonomous vehicle. Using the BB robotic principles, Blackmore et al. (2002) developed a system architecture for the behavioral control of an autonomous tractor. Blackmore followed the assumption that robotic architecture designs refer to a software architecture, rather than hardware, side of the system (Arkin, 1998). A more practical approach of control architecture for ag-robots was proposed by Torrie et al. (2002). The authors stated that, to effectively support Unmanned Ground Vehicle (UGV) systems, a standard architecture is required. They developed a Joint Architecture for Unmanned Ground Systems (JAUGS) that was independent of vehicle platform, mission or tasks, computer hardware and technology. JAUGS was scalable and can be applied for any task with minimum components. This architecture was implemented on orchard tractors and small utility vehicles. In a similar attempt, a system architecture that connects high level and low level controllers of a robotic vehicle was proposed by Mott et al. (2009). In addition to the high and low-level controllers, a middle-level was introduced that acted as a communication bridge integrating the high and low-level controllers thereby providing robustness to the robotic vehicles.
What is needed in the art therefore is a generalized control architecture of an ag-robot that provides intelligence to the ag-robot enabling full autonomy and sufficient intelligence to accomplish a desired task while encountering unpredictable situations.